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Simulation-based Algorithms for Markov Decision Processes - Hyeong Soo Chang
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Hyeong Soo Chang:

Simulation-based Algorithms for Markov Decision Processes - neues Buch

ISBN: 9781849966436

ID: 978184996643

Markov decision process (MDP) models are widely used for modeling sequential decision-making problems that arise in engineering, economics, computer science, and the social sciences. It is well-known that many real-world problems modeled by MDPs have huge state and/or action spaces, leading to the notorious curse of dimensionality that makes practical solution of the resulting models intractable. In other cases, the system of interest is complex enough that it is not feasible to specify some of the MDP model parameters explicitly, but simulation samples are readily available (e.g., for random transitions and costs). For these settings, various sampling and population-based numerical algorithms have been developed recently to overcome the difficulties of computing an optimal solution in terms of a policy and/or value function. Specific approaches include:. multi-stage adaptive sampling;. evolutionary policy iteration;. evolutionary random policy search; and. model reference adaptive search. Simulation-based Algorithms for Markov Decision Processes brings this state-of-the-art research together for the first time and presents it in a manner that makes it accessible to researchers with varying interests and backgrounds. In addition to providing numerous specific algorithms, the exposition includes both illustrative numerical examples and rigorous theoretical convergence results. The algorithms developed and analyzed differ from the successful computational methods for solving MDPs based on neuro-dynamic programming or reinforcement learning and will complement work in those areas. Furthermore, the authors show how to combine the various algorithms introduced with approximate dynamic programming methods that reduce the size of the state space and ameliorate the effects of dimensionality.The self-contained approach of this book will appeal not only to researchers in MDPs, stochastic modeling and control, and simulation but will be a valuable source of instruction and reference for students of control and operations research. Hyeong Soo Chang, Books, Business and Finance, Management and Leadership, Operations Research, Simulation-based Algorithms for Markov Decision Processes Books>Business and Finance>Management and Leadership>Operations Research, Springer London

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Chang, H: Simulation-based Algorithms for Markov Decision Pr - Hyeong Soo Chang#Michael C. Fu#Jiaqiao Hu#Steven I. Marcus
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Chang, H: Simulation-based Algorithms for Markov Decision Pr - neues Buch

2010, ISBN: 9781849966436

ID: 692119647

Markov decision process (MDP) models are widely used for modeling sequential decision-making problems that arise in engineering, economics, computer science, and the social sciences. It is well-known that many real-world problems modeled by MDPs have huge state and/or action spaces, leading to the notorious curse of dimensionality that makes practical solution of the resulting models intractable. In other cases, the system of interest is complex enough that it is not feasible to specify some of the MDP model parameters explicitly, but simulation samples are readily available (e.g., for random transitions and costs). For these settings, various sampling and population-based numerical algorithms have been developed recently to overcome the difficulties of computing an optimal solution in terms of a policy and/or value function. Specific approaches include: multi-stage adaptive sampling; evolutionary policy iteration; evolutionary random policy search; and model reference adaptive search. Simulation-based Algorithms for Markov Decision Processes brings this state-of-the-art research together for the first time and presents it in a manner that makes it accessible to researchers with varying interests and backgrounds. In addition to providing numerous specific algorithms, the exposition includes both illustrative numerical examples and rigorous theoretical convergence results. The algorithms developed and analyzed differ from the successful computational methods for solving MDPs based on neuro-dynamic programming or reinforcement learning and will complement work in those areas. Furthermore, the authors show how to combine the various algorithms introduced with approximate dynamic programming methods that reduce the size of the state space and ameliorate the effects of dimensionality. The self-contained approach of this book will appeal not only to researchers in MDPs, stochastic modeling and control, and simulation but will be a valuable source of instruction and reference for students of control and operations research. Chang, H: Simulation-based Algorithms for Markov Decision Pr Bücher > Fremdsprachige Bücher > Englische Bücher Taschenbuch 19.10.2010, Springer, .201

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Chang, H: Simulation-based Algorithms for Markov Decision Pr - neues Buch

ISBN: 9781849966436

ID: 3781d45d4103ac51879846494a15face

Chang, H: Simulation-based Algorithms for Markov Decision Pr Markov decision process (MDP) models are widely used for modeling sequential decision-making problems that arise in engineering, economics, computer science, and the social sciences. It is well-known that many real-world problems modeled by MDPs have huge state and/or action spaces, leading to the notorious curse of dimensionality that makes practical solution of the resulting models intractable. In other cases, the system of interest is complex enough that it is not feasible to specify some of the MDP model parameters explicitly, but simulation samples are readily available (e.g., for random transitions and costs). For these settings, various sampling and population-based numerical algorithms have been developed recently to overcome the difficulties of computing an optimal solution in terms of a policy and/or value function. Specific approaches include: multi-stage adaptive sampling; evolutionary policy iteration; evolutionary random policy search; and model reference adaptive search. Simulation-based Algorithms for Markov Decision Processes brings this state-of-the-art research together for the first time and presents it in a manner that makes it accessible to researchers with varying interests and backgrounds. In addition to providing numerous specific algorithms, the exposition includes both illustrative numerical examples and rigorous theoretical convergence results. The algorithms developed and analyzed differ from the successful computational methods for solving MDPs based on neuro-dynamic programming or reinforcement learning and will complement work in those areas. Furthermore, the authors show how to combine the various algorithms introduced with approximate dynamic programming methods that reduce the size of the state space and ameliorate the effects of dimensionality. The self-contained approach of this book will appeal not only to researchers in MDPs, stochastic modeling and control, and simulation but will be a valuable source of instruction and reference for students of control and operations research. Bücher / Fremdsprachige Bücher / Englische Bücher 978-1-84996-643-6, Springer

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Markov decision process (MDP) models are widely used for modeling sequential decision-making problems that arise in engineering, economics, computer science, and the social sciences. It is well-known that many real-world problems modeled by MDPs have huge state and/or action spaces, leading to the notorious curse of dimensionality that makes practical solution of the resulting models intractable. In other cases, the system of interest is complex enough that it is not feasible to specify some of the MDP model parameters explicitly, but simulation samples are readily available (e.g., for random transitions and costs). For these settings, various sampling and population-based numerical algorithms have been developed recently to overcome the difficulties of computing an optimal solution in terms of a policy and/or value function. Specific approaches include: multi-stage adaptive sampling; evolutionary policy iteration; evolutionary random policy search; and model reference adaptive search. Simulation-based Algorithms for Markov Decision Processes brings this state-of-the-art research together for the first time and presents it in a manner that makes it accessible to researchers with varying interests and backgrounds. In addition to providing numerous specific algorithms, the exposition includes both illustrative numerical examples and rigorous theoretical convergence results. The algorithms developed and analyzed differ from the successful computational methods for solving MDPs based on neuro-dynamic programming or reinforcement learning and will complement work in those areas. Furthermore, the authors show how to combine the various algorithms introduced with approximate dynamic programming methods that reduce the size of the state space and ameliorate the effects of dimensionality. The self-contained approach of this book will appeal not only to researchers in MDPs, stochastic modeling and control, and simulation but will be a valuable source of instruction and reference for students of control and operations research. Chang, H: Simulation-based Algorithms for Markov Decision Pr Buch (fremdspr.) Bücher>Fremdsprachige Bücher>Englische Bücher, Springer

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Simulation-based Algorithms for Markov Decision Processes - Hyeong Soo Chang; Michael C. Fu; Jiaqiao Hu; Steven I. Marcus
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Simulation-based Algorithms for Markov Decision Processes
Autor:

Chang, Hyeong Soo; Marcus, Steven I.; Hu, Jiaqiao; Fu, Michael C.

Titel:

Simulation-based Algorithms for Markov Decision Processes

ISBN-Nummer:

9781849966436

Markov decision process (MDP) models are widely used for modeling sequential decision-making problems that arise in engineering, economics, computer science, and the social sciences. This book brings the state-of-the-art research together for the first time. It provides practical modeling methods for many real-world problems with high dimensionality or complexity which have not hitherto been treatable with Markov decision processes.

Detailangaben zum Buch - Simulation-based Algorithms for Markov Decision Processes


EAN (ISBN-13): 9781849966436
ISBN (ISBN-10): 1849966435
Taschenbuch
Erscheinungsjahr: 2010
Herausgeber: Springer-Verlag GmbH
208 Seiten
Gewicht: 0,322 kg
Sprache: eng/Englisch

Buch in der Datenbank seit 16.02.2011 16:29:30
Buch zuletzt gefunden am 13.11.2016 22:03:18
ISBN/EAN: 9781849966436

ISBN - alternative Schreibweisen:
1-84996-643-5, 978-1-84996-643-6

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